Self-Referential Memory for the Big-Five Personality Traits

Scully, Iiona D.; Terry, Christopher P.

doi:10.24839/1089-4136.jn16.3.123

Cited by 2 publications

(1 citation statement)

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“…While grouping individuals by SRJs has been applied in the field of personality ( Scully and Terry, 2011 ), it has not been applied clinically to depression despite evidence that SRJs have important prognostic value ( Nejad et al, 2013 ; LeMoult et al, 2017 ) both from the perspectives of self-referential processing and psychodynamic constructs of depression. In applying clustering of SRJs across clinical and non-clinical populations, our findings may further our understanding of how these very different theoretical frameworks relate to one another.…”

Section: Discussionmentioning

confidence: 99%

Transdiagnostic clustering of self-schema from self-referential judgements identifies subtypes of healthy personality and depression

Tan,

Wang,

Tan

et al. 2024

Front. Neuroinform.

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IntroductionThe heterogeneity of depressive and anxiety disorders complicates clinical management as it may account for differences in trajectory and treatment response. Self-schemas, which can be determined by Self-Referential Judgements (SRJs), are heterogeneous yet stable. SRJs have been used to characterize personality in the general population and shown to be prognostic in depressive and anxiety disorders.MethodsIn this study, we used SRJs from a Self-Referential Encoding Task (SRET) to identify clusters from a clinical sample of 119 patients recruited from the Institute of Mental Health presenting with depressive or anxiety symptoms and a non-clinical sample of 115 healthy adults. The generated clusters were examined in terms of most endorsed words, cross-sample correspondence, association with depressive symptoms and the Depressive Experiences Questionnaire and diagnostic category.ResultsWe identify a 5-cluster solution in each sample and a 7-cluster solution in the combined sample. When perturbed, metrics such as optimum cluster number, criterion value, likelihood, DBI and CHI remained stable and cluster centers appeared stable when using BIC or ICL as criteria. Top endorsed words in clusters were meaningful across theoretical frameworks from personality, psychodynamic concepts of relatedness and self-definition, and valence in self-referential processing. The clinical clusters were labeled “Neurotic” (C1), “Extraverted” (C2), “Anxious to please” (C3), “Self-critical” (C4), “Conscientious” (C5). The non-clinical clusters were labeled “Self-confident” (N1), “Low endorsement” (N2), “Non-neurotic” (N3), “Neurotic” (N4), “High endorsement” (N5). The combined clusters were labeled “Self-confident” (NC1), “Externalising” (NC2), “Neurotic” (NC3), “Secure” (NC4), “Low endorsement” (NC5), “High endorsement” (NC6), “Self-critical” (NC7). Cluster differences were observed in endorsement of positive and negative words, latency biases, recall biases, depressive symptoms, frequency of depressive disorders and self-criticism.DiscussionOverall, clusters endorsing more negative words tended to endorse fewer positive words, showed more negative biases in reaction time and negative recall bias, reported more severe depressive symptoms and a higher frequency of depressive disorders and more self-criticism in the clinical population. SRJ-based clustering represents a novel transdiagnostic framework for subgrouping patients with depressive and anxiety symptoms that may support the future translation of the science of self-referential processing, personality and psychodynamic concepts of self-definition to clinical applications.

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Section: Discussionmentioning

confidence: 99%